This invention relates generally to process control systems and more particularly to a system which uses a performance model in real time as well as an economic model to control processes.
At the present time, the operations of industrial processes are analyzed using regression analysis, which applies curve fitting to historical data. In regression analysis, only a single dependent variable can be studied with relation to a set of independent variables. In this type of analysis, the interaction between the independent variables is not taken into account.
Neural networks, which determine the interaction between independent variables, are also used to analyze the operation of existing systems. As is known by those familiar with the art, neural networks solve problems by attempting to simulate the operation of the human brain. The brain has approximately 100 billion cells of more than 100 types, and many of the cells have more than one thousand interconnections to other cells. It can easily outperform any of the current approaches with respect to recognizing patterns and can quickly determine reasonable solutions to complex problems.
Neural network models use groups of "neurons" to emulate brain cells. The neurons are arranged in structural units or layers. In feed forward networks, each of the neurons of a layer gets an input from a preceding layer and sends an output to a succeeding one. Each neuron in a layer is connected to every other neuron of the succeeding layer. Another type of neural network has feedback from layers to preceding layers.
As is the case with the human brain, input, hidden and output layers of neurons are used. The hidden layer can be a single layer of neurons or can comprise multiple layers of neurons. Each of the neurons of the input layer receives a single input (independent variable). A weighting function is associated with, and determines the level of, each of the inputs to an input neuron.
Each neuron in the output layer represents a dependent variable whose value changes due to changes in the values of the independent variables and the interactions between the independent variables. The output of each neuron is modified by a transfer function. Various types of transfer functions, such as linear, linear threshold (which sets a maximum and minimum level to the function), step, Sigmoid (S-shaped), hyperbolic tangent or Gaussian functions can be used.
Historical data representing the independent input variables of a system are provided to the neural network model and the dependent variables (outputs) of the system are determined and compared to the actual output of the system at the time. A learning rule is inserted into the neural network which enables the neural network to reset the weighting values at the inputs of the network each time historical data is entered so that it may more accurately predict the outputs of the system. Some specific examples of the use of neural networks include improving the accuracy of the analysis of sonar signals, the diagnosis of jet engine problems, and the determination of space craft attitude. However, models which can predict plural output variables of a process based upon changes in plural input variables which interact with each other, have not been used previous to this invention, to control industrial or chemical processes in "real time" or "on-line". The term "real time", as used herein, is a process control system which either automatically changes the process, or changes the process within a short time period after the process control system indicates that a change is necessary, while the process is in progress. The term "on-line", as used herein, refers to a system which automatically receives information from, and automatically inserts changes into, the process, while the process is in progress.
In addition to the need for process control systems which operate to control processes on-line and/or in real time, there is also a need for process control systems which employ economic models in conjunction with performance models and which enable the optimization of the system to meet economic goals in addition to other types of performance goals.